Time series trend detection and forecasting using complex network topology analysis

Extracting knowledge from time series analysis has been growing in importance and complexity over the last decade as the amount of stored data has increased exponentially. Considering this scenario, new data mining techniques have continuously developed to deal with such a situation. In this paper, we propose to study time series based on its topological characteristics, observed on complex networks generated from the time series data. Specifically, the aim of the proposed model is to create a trend detection algorithm for stochastic time series based on community detection and network walk observations. It is expected that the proposed model presents some advantages over traditional time series analysis, such as dimensionality reduction, use of hidden correlation on data and reinforcement learning as more data is added to the data set. Experimental results on the Bovespa index (Brazilian stock market) trend prediction shows that the proposed technique is promising.